Accurately evaluating bone mineral density (BMD) in patients with unilateral hip osteoarthritis (OA) is crucial for diagnosing osteoporosis and selecting implants for hip arthroplasty. Our goal was to measure the BMD differences between sides, examine contributing factors, and identify the optimal side for BMD assessment in these patients. We analyzed 108 women with unilateral hip OA. Bilateral hip BMD was assessed automatically through quantitative CT (QCT) utilizing a validated, deep-learning-based approach. We evaluated BMD variations between the OA and healthy hips across total, neck, and distal regions. To determine their contributions, we analyzed factors, including patient demographics, Crowe classification, Bombelli classification, knee OA status, hip functional score, and gluteal muscle volume and density. Furthermore, we examined how side-to-side BMD differences influenced osteoporosis diagnosis using T-scores based on QCT. The average BMD on the OA side was 6.9 % lower in the total region, 14.5 % higher in the neck region, and 9.4 % lower in the distal region than on the healthy side. Contributing factors to the reduced BMD in the OA hip included younger age, Bombelli classification (atrophic type), and significant gluteal muscle atrophy. Diagnoses from the OA side revealed lower sensitivity (61 %) than those from the healthy side (88 %). Analysis on one side alone yields a more precise osteoporosis diagnosis from the healthy side. Nonetheless, bilateral BMD assessment remains crucial, particularly in younger individuals and those with atrophic OA types. Although based on QCT, our findings support bilateral analysis by dual-energy X-ray absorptiometry for these patients.

This study aimed to develop a predictive model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients by deep learning radiomic (DLRad) and clinical features. This study included 271 thyroid lobes from 228 PTC patients who underwent preoperative neck non-contrast CT at Center 1 (May 2021-April 2024). LNM status was confirmed via postoperative pathology, with each thyroid lobe labeled accordingly. The cohort was divided into training (n = 189) and validation (n = 82) cohorts, with additional temporal (n = 59 lobes, Center 1, May-August 2024) and external (n = 66 lobes, Center 2) test cohorts. Thyroid lobes were manually segmented from the isthmus midline, ensuring interobserver consistency (ICC ≥ 0.8). Deep learning and radiomics features were selected using LASSO algorithms to compute DLRad scores. Logistic regression identified independent predictors, forming DLRad, clinical, and combined models. Model performance was evaluated using AUC, calibration, decision curves, and the DeLong test, compared against radiologists' assessments. Independent predictors of LNM included age, gender, multiple nodules, tumor size group, and DLRad. The combined model demonstrated superior diagnostic performance with AUCs of 0.830 (training), 0.799 (validation), 0.819 (temporal test), and 0.756 (external test), outperforming the DLRad model (AUCs: 0.786, 0.730, 0.753, 0.642), clinical model (AUCs: 0.723, 0.745, 0.671, 0.660), and radiologist evaluations (AUCs: 0.529, 0.606, 0.620, 0.503). It also achieved the lowest Brier scores (0.167, 0.184, 0.175, 0.201) and the highest net benefit in decision-curve analysis at threshold probabilities > 20 %. The combined model integrating DLRad and clinical features exhibits good performance in predicting LNM in PTC patients.

Genome-wide association studies (GWAS) facilitate construction of polygenic risk scores (PRSs) for rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF). We investigated associations of RA and IPF PRSs with RA and high-resolution chest computed tomography (HRCT) parenchymal lung abnormalities. Participants in COPDGene, a prospective multicenter cohort of current/former smokers, had chest HRCT at study enrollment. Using genome-wide genotyping, RA and IPF PRSs were constructed using GWAS summary statistics. HRCT imaging underwent visual inspection for interstitial lung abnormalities (ILA) and quantitative CT (QCT) analysis using a machine-learning algorithm that quantified percentage of normal lung, interstitial abnormalities, and emphysema. RA was identified through self-report and DMARD use. We investigated associations of RA and IPF PRSs with RA, ILA, and QCT features using multivariable logistic and linear regression. We analyzed 9,230 COPDGene participants (mean age 59.6 years, 46.4 % female, 67.2 % non-Hispanic White, 32.8 % Black/African American). In non-Hispanic White participants, RA PRS was associated with RA diagnosis (OR 1.32 per unit, 95 %CI 1.18-1.49) but not ILA or QCT features. Among non-Hispanic White participants, IPF PRS was associated with ILA (OR 1.88 per unit, 95 %CI 1.52-2.32) and quantitative interstitial abnormalities (adjusted β=+0.50 % per unit, p = 7.3 × 10^-8) but not RA. There were no statistically significant associations among Black/African American participants. RA and IPF PRSs were associated with their intended phenotypes among non-Hispanic White participants but performed poorly among Black/African American participants. PRS may have future application to risk stratify for RA diagnosis among patients with ILD or for ILD among patients with RA.

To determine the value of on-site deep learning-based CT-derived fractional flow reserve (CT-FFR) for therapeutic management and adverse clinical outcomes in patients suspected of coronary artery disease (CAD) compared with coronary CT angiography (CCTA) alone. This single-centre prospective study included consecutive patients suspected of CAD between June 2021 and September 2021 at our hospital. Four hundred and sixty-one patients were randomized into either CT-FFR+CCTA or CCTA-alone group. The first endpoint was the invasive coronary angiography (ICA) efficiency, defined as the ICA with nonobstructive disease (stenosis <50%) and the ratio of revascularization to ICA (REV-to-ICA ratio) within 90 days. The second endpoint was the incidence of major adverse cardiaovascular events (MACE) at 2 years. A total of 461 patients (267 [57.9%] men; median age, 64 [55-69]) were included. At 90 days, the rate of ICA with nonobstructive disease in the CT-FFR+CCTA group was lower than in the CCTA group (14.7% vs 34.0%, P=.047). The REV-to-ICA ratio in the CT-FFR+CCTA group was significantly higher than in the CCTA group (73.5% vs. 50.9%, P=.036). No significant difference in ICA efficiency was found in intermediate stenosis (25%-69%) between the 2 groups (all P>.05). After a median follow-up of 23 (22-24) months, MACE were observed in 11 patients in the CT-FFR+CCTA group and 24 in the CCTA group (5.9% vs 10.0%, P=.095). The on-site deep learning-based CT-FFR improved the efficiency of ICA utilization with a similarly low rate of MACE compared with CCTA alone. The on-site deep learning-based CT-FFR was superior to CCTA for therapeutic management.

This study aims to investigate estimation of patient-specific organ doses from CT scans via radiomics feature-based SVR models with training parameter optimization, and maximize SVR models' predictive accuracy and robustness via fine-tuning regularization parameter and input feature quantities. CT images from head and abdominal scans underwent processing using DeepViewer®, an auto-segmentation tool for defining regions of interest (ROIs) of their organs. Radiomics features were extracted from the CT data and ROIs. Benchmark organ doses were then calculated through Monte Carlo (MC) simulations. SVR models, utilizing these extracted radiomics features as inputs for model training, were employed to predict patient-specific organ doses from CT scans. The trained SVR models underwent optimization by adjusting parameters for the input radiomics feature quantity and regulation parameter, resulting in appropriate configurations for accurate patient-specific organ dose predictions. The C values of 5 and 10 have made the SVR models arrive at a saturation state for the head and abdominal organs. The SVR models' MAPE and R² strongly depend on organ types. The appropriate parameters respectively are C = 5 or 10 coupled with input feature quantities of 50 for the brain and 200 for the left eye, right eye, left lens, and right lens. the appropriate parameters would be C = 5 or 10 accompanying input feature quantities of 80 for the bowel, 50 for the left kidney, right kidney, and 100 for the liver. Performance optimization of selecting appropriate combinations of input feature quantity and regulation parameters can maximize the predictive accuracy and robustness of radiomics feature-based SVR models in the realm of patient-specific organ dose predictions from CT scans.

In this work, the CT scans images of lung cancer patients are analysed to diagnose the disease at its early stage. The images are pre-processed using a series of steps such as the Gabor filter, contours to label the region of interest (ROI), increasing the sharpening and cropping of the image. Data augmentation is employed on the pre-processed images using two proposed architectures, namely (1) Convolutional Neural Network (CNN) and (2) Enhanced Integrated model for Lung Tumor Identification (EIM-LTI).•In this study, comparisons are made on non-pre-processed data, Haar and Gabor filters in CNN and the EIM-LTI models. The performance of the CNN and EIM-LTI models is evaluated through metrics such as precision, sensitivity, F1-score, specificity, training and validation accuracy.•The EIM-LTI model's training accuracy is 2.67 % higher than CNN, while its validation accuracy is 2.7 % higher. Additionally, the EIM-LTI model's validation loss is 0.0333 higher than CNN's.•In this study, a comparative analysis of model accuracies for lung cancer detection is performed. Cross-validation with 5 folds achieves an accuracy of 98.27 %, and the model was evaluated on unseen data and resulted in 92 % accuracy.

Cone Beam Computed Tomography (CBCT) has become a crucial imaging tool in modern dentistry. At present, a review does not exist to provide comprehensive understanding of the technological advancements and the entities driving the innovations of CBCT. This study aimed to analyse the patent records associated with CBCT technology, to gain valuable insights into the trends and breakthroughs, and identify key areas of focus from manufacturers. The online patent database called The Lens was accessed on 3 January 2025 to identify relevant patent records. A total of 706 patent records were identified and analysed. The majority of over 700 CBCT patents was contributed by CBCT manufacturers. The United States was the jurisdiction with most patent records, followed by Europe and China. Some manufacturers hold patent records for common features of CBCT systems, such as motion artifact correction, metal artifact reduction, reconstruction of panoramic image based on 3D data, and incorporation of artificial intelligence. Patent analysis can offer valuable insights into the development and advancement of CBCT technology, and foster collaboration between manufacturers, researchers, and clinicians. The advancements in CBCT technology, as reflected by patent trends, enhance diagnostic accuracy and treatment planning. Understanding these technological innovations can aid clinicians in selecting the most effective imaging tools for patient care.

To investigate the impacts of a deep learning-based iterative reconstruction algorithm on image quality and measuring accuracy of bone mineral density (BMD) in low-dose chest CT. Phantom and patient studies were separately conducted in this study. The same low-dose protocol was used for phantoms and patients. All images were reconstructed with filtered back projection, hybrid iterative reconstruction (HIR) (KARL®, level of 3,5,7), and deep learning-based iterative reconstruction (artificial intelligence iterative reconstruction [AIIR], low, medium, and high strength). The noise power spectrum (NPS) and the task-based transfer function (TTF) were evaluated using phantom. The accuracy and the relative error (RE) of BMD were evaluated using a European spine phantom. The subjective evaluation was performed by 2 experienced radiologists. BMD was measured using quantitative CT (QCT). Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), BMD values, and subjective scores were compared with Wilcoxon signed-rank test. The Cohen's kappa test was used to evaluate the inter-reader and inter-group agreement. AIIR reduced noise and improved resolution on phantom images significantly. There were no significant differences among BMD values in all groups of images (all P > 0.05). RE of BMD measured using AIIR images was smaller. In objective evaluation, all strengths of AIIR achieved less image noise and higher SNR and CNR (all P < 0.05). AIIR-H showed the lowest noise and highest SNR and CNR (P < 0.05). The increase in AIIR algorithm strengths did not affect BMD values significantly (all P > 0.05). The deep learning-based iterative reconstruction did not affect the accuracy of BMD measurement in low-dose chest CT while reducing image noise and improving spatial resolution. The BMD values could be measured accurately in low-dose chest CT with deep learning-based iterative reconstruction while reducing image noise and improving spatial resolution.

The objective of the present study was to determine the accuracy of machine learning (ML) models in the detection of mesiobuccal (MB2) canals in axial cone-beam computed tomography (CBCT) sections. A total of 2500 CBCT scans from the oral radiology department of University Dental Hospital, Sharjah were screened to obtain 277 high-resolution, small field-of-view CBCT scans with maxillary molars. Among the 277 scans, 160 of them showed the presence of MB2 orifice and the rest (117) did not. Two-dimensional axial images of these scans were then cropped. The images were classified and labelled as N (absence of MB2) and M (presence of MB2) by 2 examiners. The images were embedded using Google's Inception V3 and transferred to the ML classification model. Six different ML models (logistic regression [LR], naïve Bayes [NB], support vector machine [SVM], K-nearest neighbours [Knn], random forest [RF], neural network [NN]) were then tested on their ability to classify the images into M and N. The classification metrics (area under curve [AUC], accuracy, F1-score, precision) of the models were assessed in 3 steps. NN (0.896), LR (0.893), and SVM (0.886) showed the highest values of AUC with specified target variables (steps 2 and 3). The highest accuracy was exhibited by LR (0.849) and NN (0.848) with specified target variables. The highest precision (86.8%) and recall (92.5%) was observed with the SVM model. The success rates (AUC, precision, recall) of ML algorithms in the detection of MB2 were remarkable in our study. It was also observed that when the target variable was specified, significant success rates such as 86.8% in precision and 92.5% in recall were achieved. The present study showed promising results in the ML-based detection of MB2 canal using axial CBCT slices.

Prior to the commencement of treatment, it is essential to establish an objective method for accurately predicting the prognosis of patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). In this study, we aimed to develop a machine learning (ML) model to predict the response of HCC patients to TACE based on CT images analysis. Public dataset from The Cancer Imaging Archive (TCIA), uploaded in August 2022, comprised a total of 105 cases, including 68 males and 37 females. The external testing dataset was collected from March 1, 2019 to July 1, 2022, consisting of total of 26 patients who underwent TACE treatment at our institution and were followed up for at least 3 months after TACE, including 22 males and 4 females. The public dataset was utilized for ResNet50 transfer learning and ML model construction, while the external testing dataset was used for model performance evaluation. All CT images with the largest lesions in axial, sagittal, and coronal orientations were selected to construct 2.5D images. Pre-trained ResNet50 weights were adapted through transfer learning to serve as a feature extractor to derive deep features for building ML models. Model performance was assessed using area under the curve (AUC), accuracy, F1-Score, confusion matrix analysis, decision curves, and calibration curves. The AUC values for the external testing dataset were 0.90, 0.90, 0.91, and 0.89 for random forest classifier (RFC), support vector classifier (SVC), logistic regression (LR), and extreme gradient boosting (XGB), respectively. The accuracy values for the external testing dataset were 0.79, 0.81, 0.80, and 0.80 for RFC, SVC, LR, and XGB, respectively. The F1-score values for the external testing dataset were 0.75, 0.77, 0.78, and 0.79 for RFC, SVC, LR, and XGB, respectively. The ML model constructed using deep features from 2.5D images has the potential to be applied in predicting the prognosis of HCC patients following TACE treatment.